CNN was first introduced in the 1990s by Yann LeCun, which could identify handwritten digits [1]}. Initially, the use of CNN was only restricted to image processing, requiring large datasets. However, in 2012 after the success of AlexNet, which win the ImageNet challenge, showed that the time has come to revisit CNNs, as large datasets are available in the shape of ImageNet [2]}. A CNN comprises a three-dimensional set of neurons instead of the typical two-dimensional array. The first layer is called a convolutional layer, where every neuron only routes the information from a minor part of the layer. The convolution layer uses ReLU as an activation function followed by softmax. The first layer is followed by a pooling layer and where the output of the convolution layer drives to a fully connected neural network for classification. The CNNs show very promising results in the fields of image and video recognition, semantic parsing, and paraphrase detection. Fig. REF  visualizes a CNN model that first extracts all the features of the input image using the convolution operation and then uses the pooling layer to extract the most prominent features to pass through to the other convolutional layer for linearity. Later, the flatten layer shapes the input data for producing output.
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